OF: During vs After

library(tidyverse)
library(biomaRt)
library(ggrepel)
library(clusterProfiler)
library(enrichplot)
# Parallel
library(BiocParallel)
register(MulticoreParam(6))

load('../data/microarray_NGS_objects.Rdata')
load('../data/top_tables.Rdata')
sva_counts <- read_tsv('../data/sva_counts.tsv.gz')

Rows: 14318 Columns: 66
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr  (1): Gene
dbl (65): GSM2944692, GSM2944693, GSM2944694, GSM2944695, GSM2944696, GSM2944697, GSM2944698, GSM2944699, GSM2944700,...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

sample_meta_D <- sample_meta %>% filter(Sample %in% colnames(sva_counts)) %>%
  dplyr::select(Sample:Section, Layout:Fusion) %>%
  mutate(S2 = case_when(Section == 'OF' ~ 'OF', TRUE ~ 'OC')) %>%
  unique()

box_maker <- function(table, genes, section = c('OF','OC'), type = 'temporal'){
  if ('matrix' %in% class(table)){
    table <- table %>%
      as_tibble(rownames = 'Gene')
  }
  if (type == 'temporal'){
    plot <- table %>% 
      pivot_longer(-Gene, names_to = 'Sample', values_to = 'Expression') %>%
      mutate(Sample = gsub('_.*|.CEL.*','',Sample)) %>%
      left_join(sample_meta_D) %>%
      mutate(S2 = case_when(Section == 'OF' ~ 'OF',TRUE ~ 'OC')) %>%
      filter(Gene %in% genes, S2 %in% section) %>%
      #filter(Gene %in% row.names(top.table_OF_AD %>% head(10))) %>%
      mutate(Fusion = factor(Fusion, levels = c('Before','During','After'))) %>%
      mutate(OrgTech = paste(Organism, Technology, sep = '_')) %>% 
      ggplot(aes(x=Fusion, y=Expression, color = Organism, shape = Technology)) +
      # geom_boxplot(aes(group = Fusion), color = 'Black', outlier.colour = NA) +
      # geom_boxplot(aes(group = interaction(Organism,Fusion)), outlier.colour = NA) +
      ggbeeswarm::geom_quasirandom(size = 3, alpha = 0.7) +
      cowplot::theme_cowplot() +
      facet_grid(~Gene + S2, scales = 'free_y') +
      ggsci::scale_color_aaas() +
      ylab('log2 (corrected counts)') +
      stat_summary(fun=mean, geom = 'line', aes(group = OrgTech, color = Organism)) }
  else {
    plot <- table %>% 
      pivot_longer(-Gene, names_to = 'Sample', values_to = 'Expression') %>%
      mutate(Sample = gsub('_.*|.CEL.*','',Sample)) %>%
      left_join(sample_meta_D) %>%
      mutate(S2 = case_when(Section == 'OF' ~ 'OF',TRUE ~ 'OC')) %>%
      filter(Gene %in% genes, S2 %in% section) %>%
      mutate(Fusion = factor(Fusion, levels = c('Before','During','After'))) %>%
      filter(Fusion == 'During') %>% 
      mutate(OrgTech = paste(Organism, Technology, sep = '_')) %>% 
      ggplot(aes(x=S2, y=Expression, color = Organism, shape = Technology)) +
      # geom_boxplot(aes(group = Fusion), color = 'Black', outlier.colour = NA) +
      # geom_boxplot(aes(group = interaction(Organism,Fusion)), outlier.colour = NA) +
      ggbeeswarm::geom_quasirandom(size = 3, alpha = 0.7) +
      cowplot::theme_cowplot() +
      ggsci::scale_color_aaas() +
      ylab('log2 (corrected counts)') +
      xlab('Section') +
      stat_summary(fun=mean, geom = 'line', aes(group = OrgTech, color = Organism)) + facet_wrap(~Gene)
  }
  plot
}

volcano_maker <- function(df, 
                          title="Volcano Plot", 
                          pvalue='P.Value', 
                          padj='adj.P.Val', 
                          logFC='logFC', 
                          gene_list = ''){
  df$pvalue <- df[,pvalue]
  df$log2FoldChange <- df[,logFC]
  df$padj <- df[,padj]
  df$Gene <- row.names(df)
  df <- df[!is.na(df$pvalue),]
  print(dim(df))
  
  df <- df %>% mutate(Class = case_when(padj < 0.05 & abs(logFC) > 1~ "FDR < 0.05 & logFC > 1",
                                        padj < 0.1 & abs(logFC) > 1 ~ 'FDR < 0.1 & logFC > 1',
                                        TRUE ~ 'Not significant'))
  df$GeneT <- df$Gene
  if (gene_list == ''){
    gene_list <- df %>% filter(padj < 0.05) %>% pull(Gene) %>% head(10)
  }
  df$Gene[!df$Gene %in% gene_list] <- ''
  
  plot <- ggplot(data=df,aes(label=Gene, x = log2FoldChange, y = -log10(pvalue))) +
    geom_point(aes(colour=Class)) +
    scale_colour_manual(values=c("darkred", "red", "grey")) +
    cowplot::theme_cowplot() +
    geom_vline(aes(xintercept=-1),linetype="dotted") +
    geom_vline(aes(xintercept=1),linetype="dotted") +
    geom_vline(aes(xintercept=-2),linetype="dotted") +
    geom_vline(aes(xintercept=2),linetype="dotted") +
    geom_label_repel(max.overlaps = 100) +
    xlab('logFC') + ylab('-log10(p value)') +
    ggtitle(title) + cowplot::theme_cowplot()
  
  plot
}

During vs After (OF)

2021-12-13

Positive means higher expression in the After relative to the During time point (among OF samples only)

Volcano

volcano_maker(top.table_OF_AD, title = 'OF: During vs After',
              
              gene_list = c(top.table_OF_AD %>% filter(logFC > 0) %>% head(12) %>% row.names(),
                            top.table_OF_AD %>% filter(logFC < 0) %>% head(12) %>% row.names()))

[1] 14318    10

Warning in if (gene_list == "") { :
  the condition has length > 1 and only the first element will be used

Diff Table

Genes with an FDR < 0.1 in this test.

top.table_OF_AD %>% as_tibble(rownames = 'Gene') %>% filter(adj.P.Val < 0.1) %>%  DT::datatable()

Expression of top 6 genes (by FDR) across section and stage

Colored by organism. Each line is drawn for organism / technology (remember, mouse has both microarray and RNA-seq).

Up (genes that go up in expression During -> After)

box_maker(sva_counts, 
          genes = top.table_OF_AD %>% 
            as_tibble(rownames = 'Gene') %>% 
            filter(adj.P.Val < 0.05, logFC > 0) %>% head(10) %>% pull(Gene),
          section = c('OF'), type = 'temporal')

Joining, by = "Sample"

Down

box_maker(sva_counts, 
          genes = top.table_OF_AD %>% 
            as_tibble(rownames = 'Gene') %>% 
            filter(adj.P.Val < 0.05, logFC < 0) %>% head(10) %>% pull(Gene),
          section = c('OF'), type = 'temporal')

Joining, by = "Sample"

Enrichment Analysis (GO, GSEA)

GSEA

GSEA uses a ranked list of genes by logFC. So the p values are not used in this situation. The order is. So the GSEA is useful in situations where there are very few differentially expressed genes.

Activated terms (higher in the “After”) relate to ion channels and cell adhesion. Suppressed terms (genes higher expressed in the During) relate to cell cycle and metabolism.

Dotplot

all_genes <- bitr(top.table_OF_AD %>% as_tibble(rownames = 'Gene') %>% filter(!grepl('RPS|RPL', Gene)) %>% pull(Gene), fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")

'select()' returned 1:many mapping between keys and columns
Warning in bitr(top.table_OF_AD %>% as_tibble(rownames = "Gene") %>% filter(!grepl("RPS|RPL",  :
  0.01% of input gene IDs are fail to map...

all_genes <- all_genes %>% left_join(top.table_OF_AD %>% as_tibble(rownames = 'SYMBOL'), by = c('SYMBOL'))

logFC <- all_genes$logFC
names(logFC) <- all_genes$ENTREZID
logFC <- na.omit(logFC)

logFC = sort(logFC, decreasing = TRUE)

gse <- gseGO(geneList=logFC,
             ont ="ALL",
             keyType = "ENTREZID",
             pvalueCutoff = 0.05,
             OrgDb = org.Hs.eg.db,
             pAdjustMethod = "BH",
             eps = 0)

preparing geneSet collections...
GSEA analysis...
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam,  :
  There are ties in the preranked stats (0.09% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
leading edge analysis...
done...

gse <- setReadable(gse, OrgDb = org.Hs.eg.db)
# change sort logic
gseF <- gse
gseF@result <- rbind(gseF@result %>% arrange(NES) %>% head(20),
                     gseF@result %>% arrange(NES) %>% tail(20) %>% arrange(-NES)
)
dotplot(gse, showCategory=15, split=".sign") + facet_grid(.~.sign) + cowplot::theme_cowplot()

Related terms

So we can see “chunks” of terms that go together. We see several terms relating to ion channels and synaptic signalling.

gsePT <- pairwise_termsim(gse)
emapplot(gsePT)

Table

So you can see the genes in the ontology term. The genes get “included” as enriched if GSEA deems them to be ranked unusually high.

gse@result %>% as_tibble() %>% arrange(-abs(NES)) %>% filter(p.adjust < 0.05) %>%  DT::datatable()

GO Enrichment

GO enrichment uses a cutoff between differentially expressed genes (FDR < 0.1 in this case) and everything else.

Loads of stuff relating to visual function and development.

diff_genes <- top.table_OF_AD %>% as_tibble(rownames = 'Gene') %>% filter(adj.P.Val < 0.1, !grepl('RPL|RPS', Gene)) 
eg_diff_genes <- bitr(diff_genes$Gene, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")

'select()' returned 1:1 mapping between keys and columns

eg_diff_genes <- diff_genes %>% left_join(., eg_diff_genes, by = c('Gene' = 'SYMBOL'))
eg_universe = bitr(top.table_OF_AD %>% as_tibble(rownames = 'Gene') %>% pull(Gene), fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")

'select()' returned 1:many mapping between keys and columns
Warning in bitr(top.table_OF_AD %>% as_tibble(rownames = "Gene") %>% pull(Gene),  :
  0.01% of input gene IDs are fail to map...

eg_diff_gene_list <- eg_diff_genes$logFC
names(eg_diff_gene_list) <- eg_diff_genes$ENTREZID

egoOF <- enrichGO(gene          = eg_diff_genes$ENTREZID,
                     universe      = eg_universe$ENTREZID,
                     OrgDb         = org.Hs.eg.db,
                     ont           = "all",
                     readable      = TRUE)



p1 <- dotplot(egoOF, showCategory=20) + ggtitle("Dotplot for GO")
p1

NA
NA

Table

So you can see the genes in the ontology term.

egoOF@result %>% as_tibble() %>%  filter(p.adjust < 0.05) %>%  DT::datatable()

CNET Plot

Relationships between related GO terms with shared genes. Yellow means more expressed in the OF than the OC.

geneList <- eg_diff_genes$logFC
names(geneList) <- eg_diff_genes$Gene
cnet <- cnetplot(egoOF, foldChange = geneList, showCategory = 12) + scale_color_viridis_c(name = 'log2(FoldChange)')

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

cnet

Wikipathways

# system("wget https://wikipathways-data.wmcloud.org/current/gmt/wikipathways-20211110-gmt-Homo_sapiens.gmt")
wp2gene <- read.gmt('wikipathways-20211110-gmt-Homo_sapiens.gmt')
wp2gene <- wp2gene %>% tidyr::separate(term, c("name","version","wpid","org"), "%")
wpid2gene <- wp2gene %>% dplyr::select(wpid, gene) #TERM2GENE
wpid2name <- wp2gene %>% dplyr::select(wpid, name) #TERM2NAME

ewp <- enricher(eg_diff_genes$ENTREZID,
                TERM2GENE = wpid2gene,
                TERM2NAME = wpid2name,
                pvalueCutoff = 0.1)

ewp_plot <- dotplot(ewp, showCategory=10) + ggtitle("Dotplot for WikiPathways")
ewp_plot

Table

ewp <- setReadable(ewp, OrgDb =  org.Hs.eg.db, keyType = 'ENTREZID')
ewp@result %>% DT::datatable()

KEGG Pathway Enrichment

kk <- enrichKEGG(gene         = eg_diff_genes$ENTREZID, 
                 universe = eg_universe$ENTREZID,
                 organism     = 'hsa')
dotplot(kk) + ggtitle("KEGG Pathway Enrichment") 

Table

kk <- setReadable(kk, OrgDb =  org.Hs.eg.db, keyType = 'ENTREZID')
kk@result %>% DT::datatable()

My takeaway(s)

1. Seeing a whole bunch of terms relating to cellular processes.
2. A bit of visual function terms again - so as the fissure closes get retina dev continuing…but this is comparing against the OC. Is retinal specification driving from the fissure?
3. DLX1/DLX2 interesting TF (retina cell specification as well as forebrain and drosophila head/limb stuffs)

Session Info

devtools::session_info()

─ Session info  🏙️  🙁  👨‍👧‍👦   ───────────────────────────────────────────────────────────────────────────────────────────
 hash: cityscape, slightly frowning face, family: man, girl, boy

 setting  value
 version  R version 4.1.2 (2021-11-01)
 os       macOS Catalina 10.15.7
 system   x86_64, darwin17.0
 ui       RStudio
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2021-12-14
 rstudio  2021.09.0+351 Ghost Orchid (desktop)
 pandoc   2.14.0.3 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)

─ Packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────
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 progress               1.2.2    2019-05-16 [1] CRAN (R 4.1.2)
 ps                     1.6.0    2021-02-28 [1] CRAN (R 4.1.2)
 purrr                * 0.3.4    2020-04-17 [1] CRAN (R 4.1.2)
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 RColorBrewer           1.1-2    2014-12-07 [1] CRAN (R 4.1.2)
 Rcpp                   1.0.7    2021-07-07 [1] CRAN (R 4.1.2)
 RCurl                  1.98-1.5 2021-09-17 [1] CRAN (R 4.1.2)
 readr                * 2.0.2    2021-09-27 [1] CRAN (R 4.1.2)
 readxl                 1.3.1    2019-03-13 [1] CRAN (R 4.1.0)
 remotes                2.4.1    2021-09-29 [1] CRAN (R 4.1.2)
 reprex                 2.0.1    2021-08-05 [1] CRAN (R 4.1.2)
 reshape2               1.4.4    2020-04-09 [1] CRAN (R 4.1.2)
 rlang                  0.4.12   2021-10-18 [1] CRAN (R 4.1.2)
 rmarkdown              2.11     2021-09-14 [1] CRAN (R 4.1.2)
 rpart                  4.1-15   2019-04-12 [1] CRAN (R 4.1.2)
 rprojroot              2.0.2    2020-11-15 [1] CRAN (R 4.1.2)
 RSQLite                2.2.8    2021-08-21 [1] CRAN (R 4.1.0)
 rstudioapi             0.13     2020-11-12 [1] CRAN (R 4.1.2)
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 stringi                1.7.5    2021-10-04 [1] CRAN (R 4.1.2)
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 tidyr                * 1.1.4    2021-09-27 [1] CRAN (R 4.1.2)
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 tidytree               0.3.6    2021-11-12 [1] CRAN (R 4.1.2)
 tidyverse            * 1.3.1    2021-04-15 [1] CRAN (R 4.1.2)
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 utf8                   1.2.2    2021-07-24 [1] CRAN (R 4.1.2)
 vctrs                  0.3.8    2021-04-29 [1] CRAN (R 4.1.2)
 vipor                  0.4.5    2017-03-22 [1] CRAN (R 4.1.0)
 viridis                0.6.2    2021-10-13 [1] CRAN (R 4.1.0)
 viridisLite            0.4.0    2021-04-13 [1] CRAN (R 4.1.2)
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 withr                  2.4.2    2021-04-18 [1] CRAN (R 4.1.2)
 xfun                   0.28     2021-11-04 [1] CRAN (R 4.1.2)
 XML                    3.99-0.8 2021-09-17 [1] CRAN (R 4.1.2)
 xml2                   1.3.2    2020-04-23 [1] CRAN (R 4.1.0)
 xtable                 1.8-4    2019-04-21 [1] CRAN (R 4.1.2)
 XVector                0.34.0   2021-10-26 [1] Bioconductor
 yaml                   2.2.1    2020-02-01 [1] CRAN (R 4.1.2)
 yulab.utils            0.0.4    2021-10-09 [1] CRAN (R 4.1.0)
 zlibbioc               1.40.0   2021-10-26 [1] Bioconductor

 [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library

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